#MP Moralization and Foreign Policy Attitudes Replication File: Appendix Analyses for Study 2
####Last updated: 4 June 2025

###Analyses carried out using R version 4.4.3 in RStudio version Version XXX on Lenovo ThinkPad X1 Carbon Gen 12 Intel Core Ultra 7 155U, 1700 Mhz, 12 running Windows 11 Enterprize

#### This file replicates material reported in Appendix C. Run "MP_study2_0.R" and "MP_study2_1.R" first for variable coding, functions, packages, and models!



# Appendix Section C.1: Sample --------------------------------------------

#Summarize sample characteristics
p.man <- round(table(data$man)/sum(table(data$man)), digits=3)

p.1834 <- round(table(data$age1)/sum(table(data$age1)), digits=3)[2]
p.3544 <- round(table(data$age2)/sum(table(data$age2)), digits=3)[2]
p.4564 <- round(table(data$age3)/sum(table(data$age3)), digits=3)[2]
p.65up <-  round(table(data$age4)/sum(table(data$age4)), digits=3)[2]

p.uni <- round(table(data$university)/sum(table(data$university)), digits=3)[2]

p.nwhite <- round(table(data$nhwhite)/sum(table(data$nhwhite)), digits=3)[2]
p.black <- round(table(data$black)/sum(table(data$black)), digits=3)[2]

p.reps <- round(table(data$pidreplean)/sum(table(data$pidreplean)), digits=3)[2]
p.dems <- round(table(data$piddemlean)/sum(table(data$piddemlean)), digits=3)[2]
p.inds <- round(table(data$pidtrueind)/sum(table(data$pidtrueind)), digits=3)[2]

#Combine for table:

sample.col <- rbind(p.man[2], p.man[1], p.1834, p.3544, p.4564, p.65up, p.uni, p.nwhite, p.black, p.reps, p.dems, p.inds)
sample.mat <- cbind(sample.col)
colnames(sample.mat) <- c("Sample")
rownames(sample.mat) <- c("Man", "Not Man", "18-34", "35-44", "45-64", "65+", "University", "White (not hispanic or latino)", "Black or African-American", 
                          "Republican", "Democrat", "Independent")


#### Table C1: Survey 2 Sample Characteristics
stargazer(sample.mat)

#56 participants reported a gender identity other than man/woman or preferred not to say: 
gentab2 <- table(data$gender, useNA = "ifany")
sum(head(gentab2[-c(1,2)])) # 56


# ### Appendix Section C.3: Policy Support Regression Table ---------------

#Estimate models for vaccine vignette:
vax2 <- lm(suppvax1 ~ vaxtreat*pmc_health1 + aidfirst, data=data)
vax3 <- lm(suppvax1 ~ vaxtreat*pmc_health1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data) #Add pre-registered covariates
vax4 <- lm(suppvax1 ~ vaxtreat*pmc_health1 + aidfirst +  ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data) #add MI, CI, and interest 

#Estimate models for counter-terrorism vignette:
terr2 <- lm(suppterr1 ~ terrtreat*pmc_counter1 +  aidfirst, data=data)
terr3 <- lm(suppterr1 ~ terrtreat*pmc_counter1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)#Add pre-registered covariates
terr4 <- lm(suppterr1 ~ terrtreat*pmc_counter1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data) #add MI, CI, and interest 

#Rename variables to synthesize rows when generating Table C2
names(vax1$coefficients)[names(vax1$coefficients)=="vaxtreathighaid"] <- "High Cost"
names(vax2$coefficients)[names(vax2$coefficients)=="vaxtreathighaid"] <- "High Cost"
names(vax3$coefficients)[names(vax3$coefficients)=="vaxtreathighaid"] <- "High Cost"
names(vax4$coefficients)[names(vax4$coefficients)=="vaxtreathighaid"] <- "High Cost"

names(vax2$coefficients)[names(vax2$coefficients)=="pmc_health1"] <- "Moral Conviction"
names(vax3$coefficients)[names(vax3$coefficients)=="pmc_health1"] <- "Moral Conviction"
names(vax4$coefficients)[names(vax4$coefficients)=="pmc_health1"] <- "Moral Conviction"

names(vax2$coefficients)[names(vax2$coefficients)=="vaxtreathighaid:pmc_health1"] <- "Cost x Moral"
names(vax3$coefficients)[names(vax3$coefficients)=="vaxtreathighaid:pmc_health1"] <- "Cost x Moral"
names(vax4$coefficients)[names(vax4$coefficients)=="vaxtreathighaid:pmc_health1"] <- "Cost x Moral"

names(terr1$coefficients)[names(terr1$coefficients)=="terrtreathighterr"] <- "High Cost"
names(terr2$coefficients)[names(terr2$coefficients)=="terrtreathighterr"] <- "High Cost"
names(terr3$coefficients)[names(terr3$coefficients)=="terrtreathighterr"] <- "High Cost"
names(terr4$coefficients)[names(terr4$coefficients)=="terrtreathighterr"] <- "High Cost"

names(terr2$coefficients)[names(terr2$coefficients)=="pmc_counter1"] <- "Moral Conviction"
names(terr3$coefficients)[names(terr3$coefficients)=="pmc_counter1"] <- "Moral Conviction"
names(terr4$coefficients)[names(terr4$coefficients)=="pmc_counter1"] <- "Moral Conviction"

names(terr2$coefficients)[names(terr2$coefficients)=="terrtreathighterr:pmc_counter1"] <- "Cost x Moral"
names(terr3$coefficients)[names(terr3$coefficients)=="terrtreathighterr:pmc_counter1"] <- "Cost x Moral"
names(terr4$coefficients)[names(terr4$coefficients)=="terrtreathighterr:pmc_counter1"] <- "Cost x Moral"


##Generate Table C2: Costs, Moral Conviction, and Policy Support
stargazer(vax1, vax2, vax3, vax4, terr1, terr2, terr3, terr4, title="Costs, Moral Conviction, and Policy Support",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_support", 
          dep.var.labels=c("Support Global Vaccine Aid", "Support Counter-terrorism"),
          covariate.labels = c("High Cost", "Moral Conv.", "Aid First", 
                               "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"), notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex



# ### Appendix Section C.4: Participatory Engagement Regression Tables --------

# models a1 and a2 estimated in MP_study2_1!
vaxa3 <- lm(actions_vax ~ vaxtreat*pmc_health1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)
vaxa4 <- lm(actions_vax ~ vaxtreat*pmc_health1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)

terra3 <- lm(actions_terr ~ terrtreat*pmc_counter1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)
terra4 <- lm(actions_terr ~ terrtreat*pmc_counter1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)


###Make table

#Rename variables to avoid label repetition in tables
names(vaxa1$coefficients)[names(vaxa1$coefficients)=="vaxtreathighaid"] <- "High Cost"
names(vaxa2$coefficients)[names(vaxa2$coefficients)=="vaxtreathighaid"] <- "High Cost"
names(vaxa3$coefficients)[names(vaxa3$coefficients)=="vaxtreathighaid"] <- "High Cost"
names(vaxa4$coefficients)[names(vaxa4$coefficients)=="vaxtreathighaid"] <- "High Cost"

names(vaxa2$coefficients)[names(vaxa2$coefficients)=="pmc_health1"] <- "Moral Conviction"
names(vaxa3$coefficients)[names(vaxa3$coefficients)=="pmc_health1"] <- "Moral Conviction"
names(vaxa4$coefficients)[names(vaxa4$coefficients)=="pmc_health1"] <- "Moral Conviction"


names(vaxa2$coefficients)[names(vaxa2$coefficients)=="vaxtreathighaid:pmc_health1"] <- "Cost x Moral"
names(vaxa3$coefficients)[names(vaxa3$coefficients)=="vaxtreathighaid:pmc_health1"] <- "Cost x Moral"
names(vaxa4$coefficients)[names(vaxa4$coefficients)=="vaxtreathighaid:pmc_health1"] <- "Cost x Moral"


names(terra1$coefficients)[names(terra1$coefficients)=="terrtreathighterr"] <- "High Cost"
names(terra2$coefficients)[names(terra2$coefficients)=="terrtreathighterr"] <- "High Cost"
names(terra3$coefficients)[names(terra3$coefficients)=="terrtreathighterr"] <- "High Cost"
names(terra4$coefficients)[names(terra4$coefficients)=="terrtreathighterr"] <- "High Cost"

names(terra2$coefficients)[names(terra2$coefficients)=="pmc_counter1"] <- "Moral Conviction"
names(terra3$coefficients)[names(terra3$coefficients)=="pmc_counter1"] <- "Moral Conviction"
names(terra4$coefficients)[names(terra4$coefficients)=="pmc_counter1"] <- "Moral Conviction"

names(terra2$coefficients)[names(terra2$coefficients)=="terrtreathighterr:pmc_counter1"] <- "Cost x Moral"
names(terra3$coefficients)[names(terra3$coefficients)=="terrtreathighterr:pmc_counter1"] <- "Cost x Moral"
names(terra4$coefficients)[names(terra4$coefficients)=="terrtreathighterr:pmc_counter1"] <- "Cost x Moral"


### Table C3: ####
stargazer(vaxa1, vaxa2, vaxa3, vaxa4, terra1, terra2, terra3, terra4, title="Costs, Moral Conviction, and Support for Active Measures",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_action", 
          dep.var.labels=c("Actions -- Vaccines", "Actions -- Counter-terrorism"),
          covariate.labels = c("High Cost", "Moral Conv.", "Aid First", 
                               "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"), notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex


#Estimate models for Table C4: Vaccine Participation index: Pre-treatment support subgroups
#Models a1 and a2 estimated in MPstudy2_1!

#Vaccine vignette
#Supporter subgroup:
mod.vsup.a3 <- lm(actions_vax ~ vaxtreat*pmc_health1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.vsupp)
mod.vsup.a4 <- lm(actions_vax ~ vaxtreat*pmc_health1 + aidfirst +  ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.vsupp)

#Non-supporter subgroup
mod.vopp.a3 <- lm(actions_vax ~ vaxtreat*pmc_health1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.vopp)
mod.vopp.a4 <- lm(actions_vax ~ vaxtreat*pmc_health1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.vopp)

#Combine for stargazer:
vax.sub.a <- list(mod.vsup.a1, mod.vsup.a2, mod.vsup.a3, mod.vsup.a4, mod.vopp.a1, mod.vopp.a2, mod.vopp.a3, mod.vopp.a4)


#Counter-terrorism Vignette:

#supporter subgroup:
mod.tsup.a3 <- lm(actions_terr ~ terrtreat*pmc_counter1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.tsupp)
mod.tsup.a4 <- lm(actions_terr ~ terrtreat*pmc_counter1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.tsupp)

#Non-supporter subgroup:
mod.topp.a3 <- lm(actions_terr ~ terrtreat*pmc_counter1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.topp)
mod.topp.a4 <- lm(actions_terr ~ terrtreat*pmc_counter1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.topp)

#Combine for stargazer:
terr.sub.a <- list(mod.tsup.a1, mod.tsup.a2, mod.tsup.a3, mod.tsup.a4, mod.topp.a1, mod.topp.a2, mod.topp.a3, mod.topp.a4)


### Table C4: ####
stargazer(vax.sub.a, title="Vaccine Participation Index: Pre-treatment Support Subgroups",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_vaxsubgroups", 
          covariate.labels = c("High Cost", "Vaccine Moral", "Aid First", 
                               "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"), notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex)


### Table C5: ####
stargazer(terr.sub.a, title="Counter-Terrorism Participation Index: Pre-treatment Support Subgroups",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_terrsubgroups", 
          covariate.labels = c("High Cost", "Counter-Terr. Moral", "Aid First", 
                               "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"), notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex)


# ### Appendix Section C.5: Order Effects ---------------------------------

#Vaccine vignette
#Estimate models in subset of respondents who received the vaccine vignette first
#Support DV
mod.vax1.aidfirst <- lm(suppvax1 ~ vaxtreat , data=d.aidfirst)
mod.vax2.aidfirst <- lm(suppvax1 ~ vaxtreat*pmc_health1, data=d.aidfirst)
mod.vax3.aidfirst <- lm(suppvax1 ~ vaxtreat*pmc_health1  + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.aidfirst)
mod.vax4.aidfirst <- lm(suppvax1 ~ vaxtreat*pmc_health1  +  ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.aidfirst)

#Participation index DV
mod.vax.a1.aidfirst <- lm(actions_vax ~ vaxtreat, data=d.aidfirst)
mod.vax.a2.aidfirst <- lm(actions_vax ~ vaxtreat*pmc_health1,  data=d.aidfirst)
mod.vax.a3.aidfirst <- lm(actions_vax ~ vaxtreat*pmc_health1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.aidfirst)
mod.vax.a4.aidfirst <- lm(actions_vax ~ vaxtreat*pmc_health1 + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.aidfirst)

#Combine for stargazer:
vax.mods.aidfirst <- list(mod.vax1.aidfirst, mod.vax2.aidfirst, mod.vax3.aidfirst, mod.vax4.aidfirst, mod.vax.a1.aidfirst, mod.vax.a2.aidfirst, mod.vax.a3.aidfirst, mod.vax.a4.aidfirst)

#Estimate models for the supporter and non-supporter subgroups
#Supporter subgroup
modaidfirst.vsupa1 <- lm(actions_vax ~ vaxtreat, data=d.aidfirst.vsup)
modaidfirst.vsupa2 <- lm(actions_vax ~ vaxtreat*pmc_health1, data=d.aidfirst.vsup)
modaidfirst.vsupa3 <- lm(actions_vax ~ vaxtreat*pmc_health1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.aidfirst.vsup)
modaidfirst.vsupa4 <- lm(actions_vax ~ vaxtreat*pmc_health1 + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.aidfirst.vsup)

#Non-supporters
mod.aidfirstvopp.a1 <- lm(actions_vax ~ vaxtreat, data=d.aidfirst.vopp)
mod.aidfirstvopp.a2 <- lm(actions_vax ~ vaxtreat*pmc_health1, data=d.aidfirst.vopp)
mod.aidfirstvopp.a3 <- lm(actions_vax ~ vaxtreat*pmc_health1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.aidfirst.vopp)
mod.aidfirstvopp.a4 <- lm(actions_vax ~ vaxtreat*pmc_health1 + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.aidfirst.vopp)

#Combine for stargazer:
vax.mods.aidfirst.suppgroups <- list(modaidfirst.vsupa1, modaidfirst.vsupa2, modaidfirst.vsupa3, modaidfirst.vsupa4, mod.aidfirstvopp.a1, mod.aidfirstvopp.a2, mod.aidfirstvopp.a3, mod.aidfirstvopp.a4)


#Counter-terrorism vignette
#Estimate models in subset of respondents who received the counter-terrorism vignette first
#Support DV
mod.terr1.terrfirst <- lm(suppterr1 ~ terrtreat, data=d.terrfirst)
mod.terr2.terrfirst <- lm(suppterr1 ~ terrtreat*pmc_counter1, data=d.terrfirst)
mod.terr3.terrfirst <- lm(suppterr1 ~ terrtreat*pmc_counter1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.terrfirst)
mod.terr4.terrfirst <- lm(suppterr1 ~ terrtreat*pmc_counter1 + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.terrfirst)

#Participation index DV
mod.terr.a1.terrfirst <- lm(actions_terr ~ terrtreat, data=d.terrfirst)
mod.terr.a2.terrfirst <- lm(actions_terr ~ terrtreat*pmc_counter1, data=d.terrfirst)
mod.terr.a3.terrfirst <- lm(actions_terr ~ terrtreat*pmc_counter1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.terrfirst)
mod.terr.a4.terrfirst <- lm(actions_terr ~ terrtreat*pmc_counter1 + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=d.terrfirst)

#Combine for stargazer:
terr.mods.terrfirst <- list(mod.terr1.terrfirst, mod.terr2.terrfirst, mod.terr3.terrfirst, mod.terr4.terrfirst, mod.terr.a1.terrfirst, mod.terr.a2.terrfirst, mod.terr.a3.terrfirst, mod.terr.a4.terrfirst)


#Estimate models for the supporter and non-supporter subgroups
#Supporter subgroup
mod.terrfirst.tsup.a1 <- lm(actions_terr ~ terrtreat, data=dat.terrfirst.tsupp)
mod.terrfirst.tsup.a2 <- lm(actions_terr ~ terrtreat*pmc_counter1, data=dat.terrfirst.tsupp)
mod.terrfirst.tsup.a3 <- lm(actions_terr ~ terrtreat*pmc_counter1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.terrfirst.tsupp)
mod.terrfirst.tsup.a4 <- lm(actions_terr ~ terrtreat*pmc_counter1 + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.terrfirst.tsupp)

#Non-supporters
mod.terrfirst.topp.a1 <- lm(actions_terr ~ terrtreat, data=dat.terrfirst.topp)
mod.terrfirst.topp.a2 <- lm(actions_terr ~ terrtreat*pmc_counter1, data=dat.terrfirst.topp)
mod.terrfirst.topp.a3 <- lm(actions_terr ~ terrtreat*pmc_counter1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.terrfirst.topp)
mod.terrfirst.topp.a4 <- lm(actions_terr ~ terrtreat*pmc_counter1 + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=dat.terrfirst.topp)


#Combine for stargazer:
terr.mods.terrfirst.suppgroups <- list(mod.terrfirst.tsup.a1, mod.terrfirst.tsup.a2, mod.terrfirst.tsup.a3, mod.terrfirst.tsup.a4, mod.terrfirst.topp.a1, mod.terrfirst.topp.a2, mod.terrfirst.topp.a3, mod.terrfirst.topp.a4)


### Tables ##

#### Table C6: Subgroup receiving vaccine scenario first
stargazer(vax.mods.aidfirst,
          title="Subgroup Receiving Vaccine Scenario First",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_vaxfirst", 
          covariate.labels = c("High Cost", "Vaccine Moral", "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"), notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex)


### Table C7: Subgroup receiving counter-terrorism scenario first
stargazer(terr.mods.terrfirst,
          title="Subgroup Receiving Counter-terrorism Scenario First",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_terrfirst", 
          covariate.labels = c("High Cost", "Vaccine Moral", "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"), notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex)


### Table C8: Vaccine Participation Index: Pre-treatment Support Subgroups, Vaccine Experiment
stargazer(vax.mods.aidfirst.suppgroups, title="Vaccine Participation Index: Pre-treatment Support Subgroups, Vaccine Experiment First",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_vaxsubgroupsaidfirst", 
          covariate.labels = c("High Cost", "Vaccine Moral",  "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"),  notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex)


### Table C9: Counter-terrorism Participation Index: Pre-treatment Support Subgroups, Terrorism Experiment First
stargazer(terr.mods.terrfirst.suppgroups, 
          title="Counter-Terrorism Participation Index: Pre-treatment Support Subgroups",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_terrfirst_terrsubgroups", 
          covariate.labels = c("High Cost", "Counter-Terr. Moral",  
                               "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"),  notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex)




# ### Appendix Section C.6: Post-treatment conviction and care ------------

#Vaccine vignette
#post-treatment moral conviction
vax.f1 <- lm(fund_vax1 ~ vaxtreat + aidfirst, data=data)
vax.f2 <- lm(fund_vax1 ~ vaxtreat*pmc_health1 + aidfirst, data=data)
vax.f3 <- lm(fund_vax1 ~ vaxtreat*pmc_health1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)
vax.f4 <- lm(fund_vax1 ~ vaxtreat*pmc_health1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)

#Care about the issue
vax.c1 <- lm(care_vax1 ~ vaxtreat + aidfirst, data=data)
vax.c2 <- lm(care_vax1 ~ vaxtreat*pmc_health1 + aidfirst, data=data)
vax.c3 <- lm(care_vax1 ~ vaxtreat*pmc_health1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)
vax.c4 <- lm(care_vax1 ~ vaxtreat*pmc_health1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)

#combine for stargazer
vax.mods.mccare <- list(vax.f1, vax.f2, vax.f3, vax.f4, vax.c1, vax.c2, vax.c3, vax.c4)

#Counter-terrorism vignette
#post-treatment moral conviction - f2 and c2 estimated in MP_study2_1!
terr.f1 <- lm(fund_terror1 ~ terrtreat + aidfirst, data=data)
terr.f3 <- lm(fund_terror1 ~ terrtreat*pmc_counter1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)
terr.f4 <- lm(fund_terror1 ~ terrtreat*pmc_counter1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)

#Care about the issue
terr.c1 <- lm(care_terr1 ~ terrtreat + aidfirst, data=data)
terr.c3 <- lm(care_terr1 ~ terrtreat*pmc_counter1 + aidfirst + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)
terr.c4 <- lm(care_terr1 ~ terrtreat*pmc_counter1 + aidfirst + ci1 + mi1 + polint1 + pidreplean + piddemlean + man + age1 + age2 + age3 + nhwhite + university, data=data)

#Combine for stargazer: 
terr.mods.mccare <- list(terr.f1, terr.f2, terr.f3, terr.f4, terr.c1, terr.c2, terr.c3, terr.c4)


### Table C10: ###
stargazer(vax.mods.mccare, title="Vaccine Aid: Post-treatment Moral Conviction and Care",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_ptmcvax", 
          covariate.labels = c("High Cost", "Vaccine Moral", "Aid First", 
                               "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          dep.var.labels = c("Moral Conviction", "Care about Issue"),
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"),  notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex)

### Table C11: ###
stargazer(terr.mods.mccare, title="Counter-terrorism: Post-treatment Moral Conviction and Care",
          omit.stat=c("LL", "ser", "f", "adj.rsq"), style="apsr", digits=2, label="tab:s2_ptmcterr", 
          covariate.labels = c("High Cost", "Counter terr. Moral", "Aid First", 
                               "CI",  "MI", "Pol. Interest", "Republican", "Democrat",
                               "Man", "18-34", "35-44", "45-64", "White", "University", "Cost x Moral"), 
          dep.var.labels = c("Moral Conviction", "Care about Issue"),
          star.cutoffs = c(0.1, 0.05, 0.01), star.char=c("\\dagger", "*", "**"),  notes="$^{\\dagger}$p $<$ .1; $^{*}$p $<$ .05; $^{**}$p $<$ .01") #note replaces the legend for p-values; remove the row above it when formatting in tex)


